AI is changing everything and humans are biologically built to resist change. This is the workplace tension playing out in boardrooms across industries, fueled by conflicting data on what AI actually means for jobs, productivity, and the future of work.

As a globally recognized innovation strategist, Shawn Kanungo has spent nearly two decades helping organizations navigate exactly this kind of disruption — first at Deloitte where for 12 years he worked directly with leaders to find the opportunity inside industry-wide change, and now as one of the most in-demand keynote speakers on AI and innovation. He is the bestselling author of The Bold Ones, a book McKinsey named essential reading for decision-makers, and the force behind the provocative television special of the same name, now streaming on Apple TV and Amazon Prime.

Having worked with Fortune 500 leaders across industries, Shawn breaks down what’s really separating the companies winning with AI from those falling behind — and what others can learn from them.

Getting AI Right

Seeing the ROI on AI begins with commitment, Shawn says. Organizations that are “100% in” — top-down and bottom-up, every process on the table — are the ones outperforming their competition 10x.  “They’re seeing the returns. They’re seeing the fluency and productivity” that the laggards just aren’t, Shawn says.

But they aren’t just mandating AI company-wide, Shawn adds. They’re building experimentation into the structure of their organization: lunch-and-learns, internal champions, grassroots wins that get celebrated. “There’s nothing more motivating than seeing Sally in accounting build an incredible automation tool for herself,” Shawn says.

Those just dipping their toes in — a pilot here, a tool there without full commitment — are the ones struggling, because the promised returns never materialize. Part of the problem is the noise itself: conflicting data on whether AI is killing entry-level jobs or just changing what they look like, on who actually benefits from the productivity gains, on whether the payoff is even real. That noise gives leaders an excuse to keep testing the water instead of diving in.

The organizations doing it right are seeing the opposite. Instead of fewer jobs, they’re adding them — companies making the biggest AI investments are growing headcount by roughly 10% over two years, according to Ramp Economics Lab, the research arm of financial operations platform Ramp. “When you automate tasks, you find new value. And to extract more value, you need to hire more people,” Shawn says.

Where Most Organizations Get It Backwards

While commitment is key, the foundation they start from is just as crucial. Most organizations start with the tool — pick a model, roll it out, see what happens. Shawn says that’s the wrong order: get the business fundamentals right first, and the AI takes care of itself.

“Right now, everyone has LeBron James at their fingertips,” Shawn says. “But if you don’t surround LeBron with the right people and the right process, he’ll be useless. The same with AI. Claude, OpenAI, they’re incredible models. But you have to surround these models with the right scaffolding.”

Building the Right Scaffolding

When implementing AI, Shawn recommends organizations follow these three steps:

1. Get Your Fundamentals in Order

This starts with mapping out your existing processes. “The beauty of AI isn’t automation — it’s the ability to reimagine how you run a process. You can’t reimagine what you don’t understand,” Shawn says. Next, know where your organization is going over the next five years, and actually document that strategy. Then, get your data in order. If you put garbage in, garbage will come out. “The best operators are the best AI operators,” Shawn says. Get the strategy, the process, and the data right, and AI will pull it all together to find efficiencies and opportunities.

The important thing to remember is that you don’t have to wait for perfection. “You’re never going to have the perfect strategy, perfect data, and perfect processes,” Shawn says. “The real risk is waiting too long.” Get it to good enough and run with it.

2. Run Small, Fast Pilots

Identify a narrow problem, test it with a small team, and have the courage to look honestly at what worked and what didn’t before scaling. Working with AI is like building a muscle. Discomfort is part of the process: a pilot that never gets scrutinized honestly teaches an organization nothing. Run it, watch it closely, and be willing to kill it if it doesn’t work — then take what you learned and expand from there.

3. Push AI Further Than Feels Comfortable

“The real unlock is asking: how can AI do things we’ve never been able to do before — not just things we already do, faster.” Most organizations stop short of that, Shawn says. They use AI to automate what they’re already doing and call it a win, without asking the bigger question: what could we do that we’ve never been able to do at all?

He points to his own father, a solo-practice accountant. No employee could audit every client transaction, every day, forever — no human can do that, and no human wants to. An AI agent can. “That’s the real opportunity,” Shawn says. “Not doing the same work faster. Doing work that was never possible in the first place.”

AI in Action

So who is doing AI right? While there are many examples, Shawn says, particularly in customer service and back office financial process, one of his favourites is an assisted living facility. Pockets of assisted living facilities are using increasingly natural-sounding voice AI to check in with elderly residents — talking with them across languages, understanding their day-to-day challenges, and feeding that insight back to staff to build more personalized care. “AI is infinitely empathetic,” Shawn says. “A human will eventually break after the thousandth question. AI never does. It just sits there and keeps answering.”

Another example Shawn pointed to is a mortgage group out of Vancouver that took a hard look at every stage of its business — underwriting, client communication, closing — and rebuilt each one around AI. Productivity is up roughly 30% across the organization. “That’s the right lens,” Shawn says. “You’re growing, and you’re getting more productive and efficient at the same time.”

Along the same vein, a private equity firm Shawn worked with cut their time spent reviewing client materials from roughly 10 hours per transaction to a couple of minutes — freeing the team up to run more due diligence and evaluate more deals without cutting corners.

Work Scared Until You Become Scary

To any leader or organization hesitant to commit to AI, Shawn’s advice is to “work scared until you become scary.” “The reason you’re scared,” he says, “is because you haven’t immersed yourself in it. Build the muscle.”

That doesn’t mean becoming a technical expert. It means getting comfortable enough to use AI as leverage, while staying honest about what it can and can’t do for you. “AI can be an illusion. It makes us feel like an expert when we’re not. You don’t have to be an expert at everything anymore — I’m not a coder, but I can leverage AI to build. Being a deep specialist is still incredibly valuable, but you no longer need to be one at everything.”

AI isn’t about replacing expertise — it’s about amplifying it. The people who get there first are the ones worth watching. “The most dangerous person in the room is the one who understands how to leverage AI and still lead with what’s innately human. That combination — that’s what’s indispensable.”

Book Shawn Kanungo for Your Next Event

An in-demand keynote speaker, Shawn Kanungo helps leaders and teams cut through the noise and build the scaffolding to actually win with AI — bringing the same energy, clarity, and real-world case studies featured in this article.

Contact us to learn more about Shawn and how he can help you turn AI hesitation into AI advantage.